nontraining

Nontraining refers to situations or data involving the absence of training activity. The term is used in at least two distinct contexts: athletic or fitness programs, where it denotes periods of rest or reduced training load, and machine learning, where it refers to data that are not used to fit a model. The meaning is driven by the surrounding discipline.

In athletic and fitness contexts, nontraining periods include rest days, deload weeks, and off-season phases. These intervals aim to promote recovery, reduce injury risk, and support longer-term performance gains. Planning nontraining phases alongside progressive training load is common to balance stress and adaptation.

In machine learning and data science, nontraining data are datasets not used for model fitting. This typically includes validation data, used to tune hyperparameters, and test data, used to estimate generalization to new inputs. Maintaining a strict separation between training and nontraining data is essential to avoid data leakage and to obtain reliable performance estimates. Standard practice involves train/validation/test splits, and often cross-validation to maximize data use while preserving evaluation integrity.

While the term nontraining can appear in multiple domains, clarity comes from specifying the context. Ambiguity can otherwise obscure whether reference is to rest in a program or to data withheld from training for evaluation.

See also: training data, validation set, test set, overfitting, cross-validation, rest day, deload.